How to evaluate an AI app before your team adopts it
A practical checklist for separating durable AI tools from demo-ware: data handling, pricing traps, integration depth, and the signals reviewers actually trust.
Every week brings a new wave of AI tools, and most of them demo beautifully. The hard question is never whether a tool looks impressive in a launch video — it is whether it will still be earning its seat on your stack six months from now.
Start with the data path
Before you look at features, trace what happens to your data. Where is it processed, is it used for training, and can you get it deleted on request? A vendor that answers those three questions in plain language on a public page is telling you something about the whole company.
Pricing that survives success
Per-seat pricing looks harmless at five users and becomes a budget line at fifty. Model the cost at 3x your current team size, and check whether usage-based components are capped. The community reviews on AITDB flag pricing surprises more often than any other complaint.
Trust signals that matter
Verified ownership, a maker who responds to reviews, a public changelog, and a rating that holds up past fifty reviews — these are the signals that separate durable tools from demo-ware. A perfect five-star score across four reviews is noise; a steady 4.4 across four hundred is signal.